We emphasize that CycleNet loses translational symmetry, which is considered a strong feature of CNNs, especially for image classification tasks.
However, recent evidence suggests that this property may not be crucial.
Symmetries do not have to be hard coded in the architecture: they can be learned, if needed, by stochastic gradient descent [1]} and data augmentation [2]}.
Furthermore, several non-translational symmetric architectures recently achieved near state-of-the-art performance in image classification [3]}, [4]}.
The good performance of CycleNet on classification adds to this line of research, suggesting that built-in translational symmetry may not be strictly necessary.
